Papers › Smart Starts: Accelerating Convergence through Uncommon Region Exploration
Smart Starts: Accelerating Convergence through Uncommon Region Exploration
Xinyu Zhang, Mário Antunes, Tyler Estro, Erez Zadok, Klaus Mueller
Initialization profoundly affects evolutionary algorithm (EA) efficacy by dictating search trajectories and convergence. This study introduces a hybrid initialization strategy combining empty-space search algorithm (ESA) and opposition-based learning (OBL). OBL initially generates a diverse population, subsequently augmented by ESA, which identifies under-explored regions. This synergy enhances population diversity, accelerates convergence, and improves EA performance on complex, high-dimensional optimization problems. Benchmark results demonstrate the proposed method's superiority in solution quality and convergence speed compared to conventional initialization techniques.
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